bayespy.inference.vmp.nodes.wishart.WishartDistribution

class bayespy.inference.vmp.nodes.wishart.WishartDistribution[source]

Sub-classes implement distribution specific computations.

Distribution for k   imes k symmetric positive definite matrix.

\Lambda \sim \mathcal{W}(n, V)

Note: V is inverse scale matrix.

p(\Lambda | n, V) = ..

__init__(*args, **kwargs)

Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(*args, **kwargs)

Initialize self.

compute_cgf_from_parents(u_n, u_V)

CGF from parents

compute_fixed_moments_and_f(Lambda[, mask])

Compute u(x) and f(x) for given x.

compute_gradient(g, u, phi)

Compute the standard gradient with respect to the natural parameters.

compute_logpdf(u, phi, g, f, ndims)

Compute E[log p(X)] given E[u], E[phi], E[g] and E[f].

compute_message_to_parent(parent, index, …)

Compute the message to a parent node.

compute_moments_and_cgf(phi[, mask])

Return moments and cgf for given natural parameters

compute_phi_from_parents(u_n, u_V[, mask])

Compute natural parameters

compute_weights_to_parent(index, weights)

Maps the mask to the plates of a parent.

plates_from_parent(index, plates)

Resolve the plate mapping from a parent.

plates_to_parent(index, plates)

Resolves the plate mapping to a parent.

random(*params[, plates])

Draw a random sample from the distribution.

squeeze(axis)

Squeeze a plate axis from the distribution